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Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter
BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this pro...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929275/ https://www.ncbi.nlm.nih.gov/pubmed/31874602 http://dx.doi.org/10.1186/s12859-019-3258-7 |
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author | Lu, Weizhong Tang, Ye Wu, Hongjie Huang, Hongmei Fu, Qiming Qiu, Jing Li, Haiou |
author_facet | Lu, Weizhong Tang, Ye Wu, Hongjie Huang, Hongmei Fu, Qiming Qiu, Jing Li, Haiou |
author_sort | Lu, Weizhong |
collection | PubMed |
description | BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity. RESULTS: To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs. CONCLUSIONS: Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods. |
format | Online Article Text |
id | pubmed-6929275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69292752019-12-30 Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter Lu, Weizhong Tang, Ye Wu, Hongjie Huang, Hongmei Fu, Qiming Qiu, Jing Li, Haiou BMC Bioinformatics Research BACKGROUND: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity. RESULTS: To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs. CONCLUSIONS: Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods. BioMed Central 2019-12-24 /pmc/articles/PMC6929275/ /pubmed/31874602 http://dx.doi.org/10.1186/s12859-019-3258-7 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Lu, Weizhong Tang, Ye Wu, Hongjie Huang, Hongmei Fu, Qiming Qiu, Jing Li, Haiou Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter |
title | Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter |
title_full | Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter |
title_fullStr | Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter |
title_full_unstemmed | Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter |
title_short | Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter |
title_sort | predicting rna secondary structure via adaptive deep recurrent neural networks with energy-based filter |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929275/ https://www.ncbi.nlm.nih.gov/pubmed/31874602 http://dx.doi.org/10.1186/s12859-019-3258-7 |
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